MD; for the DIAMOND Study GroupBackground-Preliminary data suggest that the analysis of R-R interval variability by fractal analysis methods may provide clinically useful information on patients with heart failure. The purpose of this study was to compare the prognostic power of new fractal and traditional measures of R-R interval variability as predictors of death after acute myocardial infarction. Methods and Results-Time and frequency domain heart rate (HR) variability measures, along with short-and long-term correlation (fractal) properties of R-R intervals (exponents ␣ 1 and ␣ 2 ) and power-law scaling of the power spectra (exponent ␤), were assessed from 24-hour Holter recordings in 446 survivors of acute myocardial infarction with a depressed left ventricular function (ejection fraction Յ35%). During a meanϮSD follow-up period of 685Ϯ360 days, 114 patients died (25.6%), with 75 deaths classified as arrhythmic (17.0%) and 28 as nonarrhythmic (6.3%) cardiac deaths. Several traditional and fractal measures of R-R interval variability were significant univariate predictors of all-cause mortality. Reduced short-term scaling exponent ␣ 1 was the most powerful R-R interval variability measure as a predictor of all-cause mortality (␣ 1 Ͻ0.75, relative risk 3.0, 95% confidence interval 2.5 to 4.2, PϽ0.001). It remained an independent predictor of death (PϽ0.001) after adjustment for other postinfarction risk markers, such as age, ejection fraction, NYHA class, and medication. Reduced ␣ 1 predicted both arrhythmic death (PϽ0.001) and nonarrhythmic cardiac death (PϽ0.001). Conclusions-Analysis

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We compare three recently applied methods for analyzing heart rate variability: detrended fluctuation analysis (DFA), multiresolution wavelet analysis (WAV) and detrended time series analysis (DTS). In the comparison, both scale-dependent and scale-independent measures are considered. In agreement with recent results by Thurner et al., 9 we conclude that scale-dependent measures are well suited to separate healthy subjects from patients with heart disease. However, as regards the use in Kaplan-Meier cumulative survival curves, scaleindependent measures (generally slope values) clearly outperform scale dependent measures (generally rms values). The comparison is mainly based on a database containing recordings from 428 patients with heart disease (myocardial infarct) and on a database containing 105 healthy subjects and 11 heart patients. 315 Fractals 2000.08:315-322. Downloaded from www.worldscientific.com by UNIVERSITY OF QUEENSLAND on 02/02/15. For personal use only.